In many different fields, data acquired from instrumentation is required to be interpreted by a user to arrive at a conclusion. For instance, the data may be in the form of images, such as medical images, which are acquired by imaging apparatus and need to be interpreted by a clinician to allow or assist in diagnosis of the patient's condition. Other examples include sets of images of faces with the user being a law enforcement officer (who is required to identify whether a particular individual is present), and the interpretation of aerial images. Data processing software has been developed in various fields to assist in the interpretation of the data. For instance, in the medical imaging field, various types of software are available for enhancing the images or for deriving from the images information useful to the clinician.
It is important to note that the vast majority of medical image analysis systems are designed so that a physician interacts with them. There are numerous reasons for this, including the fact that physicians want at least to feel that they are in charge of the analysis, and because medical images are so complex that fully automatic solutions are rarely feasible. Consider a typical program A which utilises a set x of parameters that are to be set by the physician. We note that in general the set x varies (a) from patient to patient and (b) by physician P. We may write x(P, i) to indicate the dependence of the parameter set on the patient and on the physician=s preferences.
In almost all analyses of the results of applying any such program, but particularly if one wants to combine the results of applying the program in numerous different centres, or from a single centre but combining the results of usage by numerous physicians, for example as part of a multi-center drug trial, it is necessary to discount the idiosyncratic component of physician choice, in order to get at the underlying relationship between the image, clinical measurements made on that image and patient specific information (e.g. the patient's age, changes to the image, e.g. mammogram, since the last image was formed, the growth of a brain tumour . . . ). That is, it is necessary to factor out the effect of the particular physician. We refer to this goal as physician normalisation.
For instance, in the case of ultrasound images of the heart, software is available for processing the image data to detect the ventricular wall and to overlay on the image a contour corresponding to the wall. The software can also track the movement of the wall and provide quantitative measurements of the wall motion. User correction of the results of running such software is often necessary because the signals can be extremely poor in quality, e.g. noisy, and the image processing techniques are imperfect. For instance in the example of providing a contour overlying the image of the ventricular wall the user can correct the position of the contour by dragging it to where, on the basis of the user's experience, it should be. Different physicians will draw the initial contour slightly differently, in slightly different locations on the image. Each physician's opinion constitutes “physician truth” for him/her. In essence the image processing software generates from the image numeric values of some sort which have a geometric representation on the image (e.g. the contour). The user interacts with the geometric representation to correct the numeric values.
As a second instance, software is available for processing the image data of two image volumes (NMRI, CT, PET, SPECT, ultrasound, or a mixture of these) to place corresponding points in the two images in geometric correspondence or alignment. In this case, the physician is typically required to set a number of parameters, including a number of matching points, and/or the type of matching criterion, as well as setting thresholds, for example to segment the cranium in the case of CT images. As a third example, software is available for processing digitised images of mammograms (breast x-rays) and to determine a warping transformation that brings them into alignment. The mammograms may be of the same breast but taken at different times, or they may be of the left and right breasts of a patient. In this case, the physician may be asked to specify features in the two breast images that he/she judges to be in correspondence in order to initialise the subsequent operation of the program.
In all of these cases, the physician is required to interact with the program. In each such case the resulting analysis confounds the intrinsic anatomy or physiology that is of interest with the “physician truth” that embodies the physician's opinion. Similar considerations apply in many other fields of application than medicine. For example, images of the earth may be acquired from airplanes or satellites, and the resulting interpretation (for defense, environmental assessment, or prospecting for exploitable resources) often requires the intervention of a photointerpreter. In non-medical applications, we refer to “user truth” as a generalisation of physician truth. In what follows, physician truth should be considered equal to user truth and conversely.
Sometimes the interpretation of the data can be further assisted by the addition of “ground truth” which means information acquired from other sources and which is regarded as correct beyond reasonable doubt and thus very considerably reduces doubt and ambiguity about the data being considered. In the case of medical images, the “ground truth” may be in the form of biopsy data or information from other examinations. In the example using face images the “ground truth” may be previous sightings of the individual and, in the case of aerial images the “ground truth” may be data collected by an observer on the ground or in other ways.
This decision making process is illustrated in FIG. 1 of the accompanying drawings. It can be seen that data 1 (e.g. images or other signals or files), which can more generally be referred to as data from an “information domain”, is supplied to a program 3′ which produces a processed (e.g. enhanced) data set 5 perhaps by highlighting regions of an image or offering the user a tentative interpretation/diagnosis. This is used by the user 7 to produce an interpretation 9.
However, the data from the instrumentation is often extremely complex and it is difficult with such complex data to make significant improvements in the program 3 or the overall performance of the system. Furthermore, in such a system the user has an important role in framing interpretations based on the output of the program but in many cases of interest the data is complex and noisy and interpretations have to be taken on often very subtle differences. For instance, medical imaging data such as from cardiac ultrasound imaging or mammographic imaging is known to be very difficult to interpret. Although users are typically highly trained, their performance may degrade over time leading to consistent misinterpretations, or the user interaction with one type of program 3 may be worse than with a different type of program.